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copious.py

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+"""
+Convert Brat format annotations to JSONL format for NER training.
+
+Author: Amir Safari
+Date: 17.10.2025
+
+This script processes Brat annotation files (.ann and .txt) from train/dev/test
+directories and converts them into JSONL format suitable for NER model training.
+"""
+import json
+import re
+from pathlib import Path
+
+print("Starting data conversion from Brat format to JSON Lines...")
+
+# Tag mapping: Create a dictionary to convert tag names to integer IDs
+NER_TAGS = [
+    "O", "B-Taxon", "I-Taxon", "B-Geographical_Location", "I-Geographical_Location",
+    "B-Habitat", "I-Habitat", "B-Temporal_Expression", "I-Temporal_Expression",
+    "B-Person", "I-Person",
+]
+
+# Create a mapping from tag name to integer ID
+tag2id = {tag: i for i, tag in enumerate(NER_TAGS)}
+
+# Process each split directory (train, dev, test)
+for split in ["train", "dev", "test"]:
+    print(f"\nProcessing '{split}' split...")
+    input_dir = Path(split)
+    output_file = f"{split}.jsonl"
+
+    if not input_dir.exists():
+        # Skip if directory doesn't exist
+        print(f"Directory not found: {input_dir}. Skipping split.")
+        continue
+
+    with open(output_file, "w", encoding="utf-8") as outfile:
+        # Find all .ann files and process them with their corresponding .txt files
+        ann_files = sorted(input_dir.glob("*.ann"))
+        for ann_file in ann_files:
+            txt_file = ann_file.with_suffix(".txt")
+            if not txt_file.exists():
+                continue
+
+            with open(txt_file, "r", encoding="utf-8") as f:
+                # Tokenize the text by finding all non-whitespace sequences
+                text = f.read()
+
+            tokens_with_spans = [{"text": match.group(0), "start": match.start(), "end": match.end()} for match in
+                                 re.finditer(r'\S+', text)]
+            if not tokens_with_spans:
+                continue
+
+            tokens = [t["text"] for t in tokens_with_spans]
+            ner_tags = ["O"] * len(tokens)
+            # Parse the .ann file to extract entity annotations
+            with open(ann_file, "r", encoding="utf-8") as f:
+                annotations = []
+                # Apply BIO tagging scheme to tokens based on character span overlaps
+                for line in f:
+                    if not line.startswith("T"): continue
+                    parts = line.strip().split("\t")
+                    if len(parts) < 2: continue
+                    tag_info = parts[1]
+                    tag_parts = tag_info.split(" ")
+                    label = tag_parts[0].replace(" ", "_")
+                    spans_str = " ".join(tag_parts[1:])
+                    char_spans = []
+
+                    for span_part in spans_str.split(';'):
+                        try:
+                            start, end = map(int, span_part.split(' '))
+                            char_spans.append((start, end))
+                        except ValueError:
+                            continue
+                    if char_spans:
+                        annotations.append({"label": label, "spans": char_spans})
+
+            for ann in annotations:
+                is_first_token = True
+                for start_char, end_char in ann["spans"]:
+                    for i, token in enumerate(tokens_with_spans):
+                        if token["start"] < end_char and token["end"] > start_char:
+                            ner_tags[i] = f"B-{ann['label']}" if is_first_token else f"I-{ann['label']}"
+                            is_first_token = False
+
+            # Convert tag strings to integer IDs for model compatibility
+            ner_tag_ids = [tag2id.get(tag, tag2id["O"]) for tag in ner_tags]
+
+            # Write the processed example as a single JSON line
+            json_line = json.dumps({
+                "id": txt_file.stem,
+                "tokens": tokens,
+                "ner_tags": ner_tag_ids
+            })
+            outfile.write(json_line + "\n")
+
+    print(f"Successfully created {output_file}")
+
+print("\nConversion complete! ✨")

run_test.py

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+"""
+Test script to load and verify the converted JSONL dataset.
+
+Author: Amir Safari
+Date: 17.10.2025
+
+This script loads the converted JSONL files (train.jsonl, dev.jsonl, test.jsonl)
+and verifies they can be properly loaded with the HuggingFace datasets library.
+"""
+from datasets import load_dataset, Features, Value, Sequence, ClassLabel
+
+print("Attempting to load the converted JSONL dataset...")
+
+# Define all possible NER tags for the dataset
+NER_TAGS = [
+    "O", "B-Taxon", "I-Taxon", "B-Geographical_Location", "I-Geographical_Location",
+    "B-Habitat", "I-Habitat", "B-Temporal_Expression", "I-Temporal_Expression",
+    "B-Person", "I-Person",
+]
+
+# Manually define the features object for robustness
+features = Features({
+    'id': Value('string'),
+    'tokens': Sequence(Value('string')),
+    'ner_tags': Sequence(ClassLabel(names=NER_TAGS))
+})
+
+try:
+    # Load the JSON files directly using the manually defined features
+    dataset = load_dataset("json", data_files={
+        "train": "train.jsonl",
+        "validation": "dev.jsonl",
+        "test": "test.jsonl"
+    }, features=features)
+
+    print("\n✅ Success! The dataset was loaded correctly.")
+    print("Here is the loaded dataset info:")
+    print(dataset)
+
+    print("\nHere's the first training example:")
+    print(dataset["train"][0])
+
+except Exception as e:
+    print(f"\n❌ An error occurred: {e}")
+    print("Please make sure the 'train.jsonl', 'dev.jsonl', and 'test.jsonl' files exist.")